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Graduate School in Information Engineering: Ph.D. programDepartment of Information EngineeringUniversity of Padova

Course Catalogue

2011

Requirements for Ph.D. Students of the Graduate School of Information En-gineering:

1. Students are required to take courses from the present catalogue for aminimum of 80 hours (20 credits) during the first year of the Ph.D.program.

2. Students are required to take for credit at least two out of the follow-ing three basic courses “Applied Functional Analysis”, “Applied LinearAlgebra”, and “Statistical Methods” during the first year of the Ph.D.program. Moreover, the third course is strongly recommended to all stu-dents.

3. After the first year, students are strongly encouraged to take courses (pos-sibly outside the present catalogue) for at least 10 credits (or equivalent)according to their research interests.

Students have to enroll in the courses they intend to take at least one monthbefore the class starts. To enroll, it is sufficient to send an e-mail message tothe secretariat of the school at the address [email protected]

Students are expected to attend classes regularly. Punctuality is expectedboth from instructors and students.

Instructors have to report to the Director of Graduate Studies any case of astudent missing classes without proper excuse.

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Contents

1 Adaptive Control, Prof. A. Serrani 5

2 Algebraic Tools for the Identifiability of Dynamical Systems, Prof. M. P. Sac-comani 7

3 Algorithms for Bioinformatics and Computational Biology, Proff. A. Apostolicoand C. Guerra 8

4 Applied Functional Analysis, Prof. G. Pillonetto 10

5 Applied Linear Algebra (room Oe), Prof. T. Damm and Prof. H. Wimmer 12

6 Bioelectromagnetics, Prof. T. A. Minelli 13

7 Dose, Effect, Threshold, Prof. A. Trevisan 15

8 Dynamical Models in Systems Biology, Prof. C. Altafini 16

9 Dynamics over networks, Prof. F. Fagnani 17

10 E. M. Waves in Anisotropic Media, Prof. C. G. Someda 19

11 Game Theory for Information Engineering, Prof. L. Badia 21

12 Harnessing Randomness in Information Theory, Prof. M. Bloch 23

13 Information-theoretic Methods in Security, Prof. N. Laurenti 25

14 Introduction to Quantum Optics I: quantum information and communication,Prof. A. Sergienko 27

15 Introduction to Quantum Optics II: quantum measurement, imaging, and metrol-

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ogy, Prof. A. Sergienko 28

16 Multiple antennas in wireless communications, Proff. L. Sanguinetti and G.Bacci 29

17 Physical models for the numerical simulation of semiconductor devices, Prof.G. Verzellesi 31

18 Statistical Methods, Prof. L. Finesso 32

19 Stochastic (Ordinary and Partial) Differential Equations, Prof. P. Guiotto 33

20 Subspace Techniques for the Identification of Linear Systems, Prof. G. Picci 34

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1 Adaptive Control

Instructor: Andrea Serrani, The Ohio State University, Columbus, OH – USAe-mail: [email protected]

Aim: To give a broad (although necessarily incomplete) treatment of classic and more recentmethodologies for adaptive control of linear and nonlinear systems. The emphasis of the course isnot on giving a painfully detailed description of several techniques, rather on introducing funda-mental concepts and issues in the analysis and design of adaptive control systems.

Topics:

1. Overview of Adaptive Control Systems. Direct and indirect adaptive control. Theprinciple of certainty-equivalence.

2. Advanced tools for stability of non-autonomous nonlinear systems. Definitions.Converse Lyapunov theorems. LaSalle/Yoshizawa theorem. Passivity theory. Zero-state de-tectability. Input-to-state stability. Ultimate boundedness. Positive real and strictly positivereal transfer functions. Kalman-Yakubovich-Popov lemmas.

3. Stability of adaptive control systems. The role of the persistency of excitation condi-tion. Uniform observability. Exponential convergence vs. exponential stability and uniformasymptotic stability.

4. Model reference adaptive control. Parameterization of certainty-equivalence controllers.MRAC schemes for linear systems with relative degree one. Uniform global asymptotic sta-bility of MRACs. Extension to higher relative degrees.

5. Adaptive observers for linear systems. Systems in adaptive observer form. Filteredtransformations.

6. Direct adaptive control of nonlinear systems. Adaptive backstepping. Design withoverparameterization. Tuning functions method.

7. Indirect adaptive control of nonlinear systems. Modular design. Swapping lemmas.

8. Robust redesign of adaptive controller. Robustness of adaptive systems. Leakage,dead-zone and projection-based techniques.

9. Selected topics (if time permits). The adaptive regulator problem. Adaptive internalmodel design.

References: There is no official textbook for this course. Notes will be provided that cover mostof the above topics. Additional references include:

1. P. Ioannou and J. Sun. Robust Adaptive Control. Prentice Hall, Upper Saddle River, NJ, 1996.Available on line at http://www-bcf.usc.edu/ioannou/Robust_Adaptive_Control.htm

2. H. K. Khalil. Nonlinear Systems, 3rd edition. Prentice Hall, Upper Saddle River, NJ, 2001.

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3. E. Panteley, A.Loria. and A.R. Teel. Relaxed persistency of excitation for uniform asymptoticstability. IEEE Transactions on Automatic Control, 46(12):1874-1886, 2001.

4. M. Krstic. Invariant manifolds and asymptotic properties of adaptive nonlinear stabilizers.IEEE Transactions on Automatic Control, 41(6):817 - 829, 1996.

5. I. Kanellakopoulos, P.V. Kokotovic, and A.S. Morse. Systematic design of adaptive controllerrfor feedback linearizable systems. IEEE Transactions on Automatic Control, 36(11):1241-1253, 1991.

6. M. Krstic, I. Kanellakopoutos, and P. V. Kokotovic. Adaptive nonlinear control withoutover-parameterization. Systems & Control Letters, 19(3):177-185, 1992.

7. M. Krstic and P. V. Kokotovic. Control Lyapunov functions for adaptive nonlinear stabiliza-tion. Systems & Control Letters, 26(1):17-23, 1995.

8. M. Krstic and P.V. Kokotovic. Adaptive nonlinear design with controller-identifier separationand swapping. IEEE Transactions on Automatic Control, 40(3):426-440, 1995.

9. H.K. Khalil. Adaptive output feedback control of nonlinear systems represented by input-output models. IEEE Transactions on Automatic Control, 41(2):177-188, 1996.

10. A. Isidori, L. Marconi, and A. Serrani, Robust Autonomous Guidance. An Internal ModelApproach. Springer-Verlag, 2003. Chapter 1, available on line athttp://www.springer.com/engineering/book/978-1-85233-695-0

Time table: Course of 20 hours (two lectures of two hours each per week). Class meets everyThursday and Friday from 10:30 to 12:30, starting on Thursday, April 7, 2011. Room DEI/G (3-rdfloor, Dept. of Information Engineering, via Gradenigo Building).

Course requirements: A beginning graduate-level course in linear systems theory is required.Exposure to nonlinear control theory is desirable (feedback linearization, normal forms). Familiaritywith the basic concepts of Lyapunov stability theory is essential.

Examination and grading: Grading will be based on homework and a take-home final exam.

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2 Algebraic Tools for the Identifiability of Dynamical Systems

Instructor: M. P. Saccomani, Dept. of Information Engineering, University of Padova,e-mail: [email protected]

Aim: The course is intended to illustrate the modern methods used to assess a priori identifiabilityof linear and especially nonlinear dynamical systems. In particular, the course is intended to providea deep comprehension of the modern commutative algebra and differential algebra tools which canbe applied to the study of a priori identifiability of dynamic systems described by polynomial orrational equations [1, 2, 3, 4]. Some hint will be given also to application of these mathematical toolsto system and control theory problems. Emphasis will be given to systems describing biologicalphenomena [5].

Topics: State space models of polynomial and rational dynamical systems. Global and local pa-rameter identifiability. Basic concepts of commutative algebra. Grobner bases and the Buchbergeralgorithm. Basic concepts of differential algebra. The Ritt algorithm. Software tool implementa-tions. Case studies.

References

[1] B. Buchberger. Grobner Bases and System Theory. In Multidimensional Systems and SignalProcessing, Kluwer Academic Publishers, Boston (2001).

[2] K. Forsman. Constructive Commutative Algebra in Nonlinear Control Theory, Linkoping Stud-ies in Science and Technology. Dissertation No. 261, Linkoping University, Sweden (1991).

[3] L. Ljung, and S.T. Glad. On global identifiability for arbitrary model parameterizations, Auto-matica, 30, 2, 265–276 (1994).

[4] M.P. Saccomani, S. Audoly, and L. D’Angio. Parameter identifiability of nonlinear systems: therole of initial conditions, Automatica, 39, 619–632 (2004).

[5] M.P. Saccomani, S. Audoly, G. Bellu, and L. D’Angio. Testing global identifiability of biologicaland biomedical models with the DAISY software, Computers in Biology and Medicine, 40, 402–407 (2010).

Time table: Course of 16 hours. Classes (2 hours) on Monday and Wednesday 10:30-12:30. Firstlecture on Monday 7 November, 2011. (I take care of the room).

Examination and grading: Homework and a final written examination.

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3 Algorithms for Bioinformatics and Computational Biology

Instructors: Alberto Apostolico, Concettina Guerra.e-mail: [email protected]; [email protected]

Aim:The purpose of this Course is to familiarize the students with relevant and current algorithmic prob-lems of bioinformatics and computational biology, and enable them to develop tools for biologicaldata processing, and management.

Topics:Overview of DNA, RNA and Protein SequencesGenomics WEB Sites and Data Banks of Proteins, RNA and DNA Sequences, and 3D StructuresModeling, Combinatorial and Algorithmic IssuesSearching and Matching on Strings and ArraysDiscovery of Repeats, Palindromes and other Regularities in sequences and higher aggregatesRNA and Protein Structure Analysis, Prediction of protein-protein interfaces and protein bindingsitesMetabolic Networks, Protein-Protein Interaction networks. Global and local properties of biologi-cal networksAlignment of networks

References:Bibliographic Sources: selected book chapters, e.g., from the volumes listed below, and archivalresearch papers.Gusfield, D., Algorithms on Strings, Trees and Sequences - Computer Science and ComputationalBiology. Cambridge University Press, Cambridge, (1997).Pevzner, P.A., Computational Molecular Biology: an Algorithmic Approach. MIT Press, Boston(2000)Baldi, P. and Brunak, S., Bioinformatics: the Machine Learning Approach. MIT Press, Boston(2001)Apostolico, A. and Z. Galil, Pattern Matching Algorithms. Oxford Univ. Press, New York, (1997)Apostolico, A., Guerra, C., Istrail, S., Pevzner, P., and Waterman, M., RECOMB06, Proceedingsof the Tenth Conference on Research in Computational Molecular Biology, Springer LNCS Vol.3909, Berlin (2006)

Time table: Course of 20 hours (two lectures of two hours each per week). Class meets ev-ery Monday and Wednesday from 14:30 to 16:30. First lecture on Monday, March 28, 2011. RoomDEI/G (3-rd floor, Dept. of Information Engineering, via Gradenigo Building).

Course requirements:No biology background is assumed. An overall graduate level of maturity and a little programmingexperience will be expected.

Examination and grading: The course grade will be based on reading material, and individuallytailored projects.

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4 Applied Functional Analysis

Instructor: Prof. G. Pillonetto, Dept. Information Engineering, University of Padova, e-mail:[email protected]

Aim: The course is intended to give a survey of the basic aspects of functional analysis, operatortheory in Hilbert spaces, regularization theory and inverse problems.

Topics:

1. Review of some notions on metric spaces and Lebesgue integration: Metric spaces. Opensets, closed sets, neighborhoods. Convergence, Cauchy sequences, completeness. Completionof metric spaces. Review of the Lebesgue integration theory. Lebesgue spaces.

2. Banach and Hilbert spaces: Normed spaces and Banach spaces. Finite dimensional normedspaces and subspaces. Compactness and finite dimension. Bounded linear operators. Linearfunctionals. The finite dimensional case. Normed spaces of operators and the dual space.Weak topologies. Inner product spaces and Hilbert spaces. Orthogonal complements anddirect sums. Orthonormal sets and sequences. Representation of functionals on Hilbertspaces. Hilbert adjoint operator. Self-adjoint operators, unitary operators.

3. Fourier transform and convolution: The convolution product and its properties. The basicL1 and L2 theory of the Fourier transform. The inversion theorem.

4. Compact linear operators on normed spaces and their spectrum: Spectral properties of boundedlinear operators. Compact linear operators on normed spaces. Spectral properties of compactlinear operators. Spectral properties of bounded self-adjoint operators, positive operators, op-erators defined by a kernel. Mercer Kernels and Mercer’s theorem.

5. Reproducing kernel Hilbert spaces, inverse problems and regularization theory : ReproducingKernel Hilbert Spaces (RKHS): definition and basic properties. Examples of RKHS. Func-tion estimation problems in RKHS. Tikhonov regularization. Support vector regression andregularization networks. Representer theorem.

Course requirements:

1. The classical theory of functions of real variable: limits and continuity, differentiation andRiemann integration, infinite series and uniform convergence.

2. The arithmetic of complex numbers and the basic properties of the complex exponentialfunction.

3. Some elementary set theory.

4. A bit of linear algebra.

All the necessary material can be found in W. Rudin’s book Principles of Mathematical Analysis(3rd ed., McGraw-Hill, 1976). A summary of the relevant facts will be given in the first lecture.

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References:

[1] E. Kreyszig, Introductory Functional Analysis with Applications, John Wiley and Sons , 1978.

[2] M. Reed and B. Simon, Methods of Modern Mathematical Physics, vol. I, Functional Analysis,Academic Press, 1980.

[3] G. Wahba. Spline models for observational data. SIAM, 1990.

[4] C.E. Rasmussen and C.K.I. Williams. Gaussian Processes for Machine Learning. The MITPress, 2006.

Time table: Course of 28 hours (2 two-hours lectures per week): Classes on Tuesday and Thursday,10:30 – 12:30. First lecture on Tuesday October 11th, 2011. Room 318 DEI/G (3-rd floor, Dept.of Information Engineering, via Gradenigo Building).

Examination and grading: Homework assignments and final test.

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5 Applied Linear Algebra

Instructors:

Tobias Damm, TU Kaiserslautern, Germanye-mail: [email protected]

Harald Wimmer, University of Wurzburg, Germanye-mail: [email protected]

Aim: We study concepts and techniques of linear algebra that are important for applications andcomputational issues. A wide range of exercises and problems will be presented such that a practicalknowledge of tools and methods of linear algebra can be acquired.

Topics:

• Kronecker products

• Sylvester and Lyapunov matrix equations

• Least squares problems and singular value decomposition

• Computational methods

• Perturbation theory

References:

[1] E. Gregorio and L. Salce. Algebra Lineare. Edizioni Libreria Progetto, Padova, 2005.

[2] A.J. Laub. Matrix Analysis for Scientists and Engineers, SIAM, Philadelphia, 2005,

[3] C.D. Meyer. Matrix Analysis and Applied Linear Algebra, SIAM, Philadelphia, 2000.

[4] L. N. Trefethen and D. Bau Numerical Linear Algebra. SIAM, Philadelphia, 2000.

Course requirements: A good working knowledge of basic notions of linear algebra, as e.g.presented in [1].

Time table: Course of 16 hours (2 two-hours lectures per week): Classes on Tuesday and Thursday,10:30 – 12:30. First lecture on Tuesday, September 13, 2011. Classroom Oe (Dept. of InformationEngineering, via Gradenigo Building).

Examination and grading: Grading is based on homeworks or a written examination or both.

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6 Bioelectromagnetics

Instructor: Prof. Tullio A. Minelli, CIRMANMEC University of Padova, e-mail: [email protected].

Aim: Comprehension of bio-physics and bio-mathematical instruments underlying cell and tissueelectromagnetic stimulation. A phenomena survey.

Topics:

1. Basics of bioelectromagnetics.

2. Neuroelectrical phenomena.

3. Chaos, fractals, solitons and neuroelectrical signals.

4. Mobile phone radiation and neuroelectrical phenomena.

5. Neurodegeneration: Bio-physical and bio-mathematical phenomenology.

6. Mathematical models of cell membrane dynamics.

References:

[1] C. Polk and E. Postow. CRC handbook of biological effects of electromagnetic fields. BocaRaton, CRC Press 1986.

[2] C.H. Durney and D.A. Christensen. Basic introduction to bioelectromagnetics. Boca Raton,CRC Press, 2000.

[3] S. Deutsch and A. Deutsch. Understanding the nervous system. An Engineering perspective.New York: IEEE, 1993.

[4] S.S. Nagarajan. A generalized cable equation for magnetic stimulation of axons. IEEE Trans-actions on biomedical Engineering, 43, 304-312, 1996.

[5] M. Balduzzo, F. Ferro Milone, T.A. Minelli, I. Pittaro Cadore and L. Turicchia: Mathematicalphenomenology of neural synchronization by periodic fields, Nonlinear Dynamics, Psychologyand Life Sciences 7, pp.115-137, 2003.

[6] A. Vulpiani. Determinismo e caos. La nuova Italia Scientifica, Roma, 1994.

[7] T.A. Minelli, M.Balduzzo, F. Ferro Milone and V.Nofrate: Modeling cell dynamics undermobile phone radiation. Nonlinear Dynamics, Psychology and Life Sciences, to appear.

[8] Report on the potential health risk of Radiofrequency Fields (the Royal Society of Canada,2001-2003).

[9] C.P.Fall. Computational Cell Biology. Berlin, Springer, 2002.

[10] J.D. Murray. Mathematical Biology. Berlin: Springer-Verlag, 1993.

[11] Bioinitiative Report, 2007 in http://www.bioinitiative.org/report/index.htm

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Time table: Course of 12 hours plus a visit to an electro-physiology laboratory. Lectures (2 hours)on Friday 10:30 – 12:30. First lecture on Friday, January 14, 2010. Room DEI/G (3-rd floor, Dept.of Information Engineering, via Gradenigo Building).

Course requirements: None.

Examination and grading: Production of simple pedagogical circuits or measures and simula-tions of biophysical interest.

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7 Dose, effect, threshold

Instructor: Prof. Andrea Trevisan, Dipartimento di Medicina Ambientale e Sanita Pubblica,Univ. di Padova, e-mail: [email protected]

Aim: understanding of biological mechanisms that are the basis of the effect of chemical, physicaland biological agents in humans. To supply a critical evaluation of the reference data on biologicaleffects of electromagnetic fields.

Topics: General introduction to cell biology and mechanisms of pharmacokinetics. The dose andthe significance of threshold. The effect (response) of the dose. Methods to define the threshold.The significance of cancer and the threshold problem. Electromagnetic fields and general aspectsrelated to the dose and the effect.

References: Handouts provided by the instructor.

Time table: Course of 12 hours. Lectures (2 hours) on Thursday 10:30 – 12:30. First lectureon Thursday, Jan. 13, 2011. Room DEI/G (3-rd floor, Dept. of Information Engineering, viaGradenigo Building).

Course requirements: None.

Examination and grading: Oral exam.

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8 Dynamical Models in Systems Biology

Instructor: Claudio Altafini, SISSA (Int. School for Advanced Studies), Trieste.e-mail: [email protected]

Aim: The aim of the course aims is to provide an introduction to the study of dynamical models ofbiological systems, with emphasis on the analysis of nonlinear dynamics. Several of the approachesused in the study of complex biological networks will be presented.

Topics:

1. Qualitative analysis of ODE models

• linear and nonlinear systems, equilibria and (multi)stability;

• monotonicity, feedback regulation, sensitivity analysis;

• phase plane, oscillations;

• small-scale examples: single and two-species dynamics

2. Dynamical theories for biological networks:

• Stoichiometric network analysis

• Metabolic control analysis

• Chemical reaction network theory

References:

• L. Edelstein-Keshet. ”Mathematical Models in Biology”, SIAM Classics, 2005.

• E. Sontag, ”Lecture Notes in Mathematical Biology”, available online

• U. Alon, ”An Introduction to Systems Biology”, CRC press, 2007.

• B. O. Palsson, ”Systems Biology”, Cambridge Univ. Press, 2006.

• H. Othmer, “Analysis of complex networks”, Lecture Notes available online

Time table: Course of 16 hours (2 two-hours lectures per week). Class meets every Tuesday andThursday from 14:30 to 16:30. First lecture on Tuesday, February 1, 2011. Room DEI/G (3-rdfloor, Dept. of Information Engineering, via Gradenigo Building).

Course requirements: Basic courses of linear algebra and ODEs.

Examination and grading: Homeworks and final project.

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9 Dynamics over networks

Instructor: Fabio Fagnani, e-mail: [email protected]

Aim: In a large variety of scientific and technological fields (social and economic sciences, computerscience, engineering, biology), mathematical models based on networks are rapidly increasing theirimportance. The unifying setting is a large number of ’atoms’ possessing a relatively simple timedynamics, who are interconnected together. As a consequence of this interaction, complex globalproperties emerge in the network: asymptotic convergence to an equilibrium typically dependenton the initial condition, correlation phenomena on several possible network length scales, local andglobal clustering phenomena. The course wants to give an introduction to some of the hottest andmost promising research topics on dynamics over networks. The examples we have in mind andwhich we want to cover in this course include: epidemics diffusions, opinion spreading models insocial and economic networks, cooperative algorithms over sensor networks (consensus), Bayesianlearning and games over networks.

Topics:

• A bunch of examples. Social, information, technological, biological networks. Properties ofreal-world networks: small world effect, clustering, power law degree distribution, scale-freeproperties. Examples of dynamics on networks: epidemics diffusions, discrete and continuousopinion dynamics models, cooperative algorithms over sensor networks, learning models andstrategic games.

• Random graph models. Brief recap of graph concepts: paths, cycles, connectivity, geodetics,diameter, degrees. Branching processes. The Erdos-Renji model: Poisson degree distribution,giant component, phase transitions, diameter. The configuration model: graphs with givendegree distributions. Geometric random graphs. The preferential attachment model by Price-Albert-Barabasi.

• A recap of Markov chains: random walks on graphs, invariant probability distributions,convergence, spectral theory, mixing times. Discrete dynamics over networks. Epidemicdiffusions: SI and SIR models. Discrete opinion dynamics: voter model, majority models.

• Locally averaging algorithms, convergence to consensus. Gossip models. Continuous opiniondynamics models: The Hegselman-Krause and the Deffuant-Weisbuch models.

• Network learning and games. Bayesian learning on networks. Iterative models. Strategicgames on networks. Nash equilibria and convergence issues.

References:

• Jackson, Matthew O., Social and economic networks, Princeton Univ. Press, 2008.

• Vega-Redondo, Fernando, Complex social networks, Cambridge Univ. Press, 2007.

• Durrett, Richard, Random graph dynamics, Cambridge Univ. Press, 2007.

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Time table: Course of 20 hours (two lectures of two hours each per week). Class meets everyTuesday and Thursday from 10:30 to 12:30. First lecture on Tuesday, May 10, 2011. Room DEI/G(3-rd floor, Dept. of Information Engineering, via Gradenigo Building).

Course requirements: Basic probability and calculus.

Examination and grading: Homeworks.

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10 E. M. Waves in Anisotropic Media

Instructor: Carlo Giacomo Someda, Dept. of Information Engineering, Univ. of Padovae-mail: [email protected].

Aim: To go beyond the standard limitations of typical courses on e.m. waves. In all those thatare currently taught at our Department, and in comparable Universities, the medium where wavespropagate is always assumed to be linear and isotropic. This is a severe limitation, which hinders,nowadays, comprehension of how many practical devices work, as well as of some physical conceptsthat are relevant even to philosophy of science (e.g., reciprocity).

Topics:

1. Features shared by all linear anisotropic media: mathematical description in terms of dyadics.Energy transport in anisotropic media: waves and rays.

2. Propagation of polarization in anisotropic media: the Jones matrix, the Mueller matrix.

3. Linearly birefringent media: Fresnels equations of wave normals. The indicatrix.

4. Applications: fundamentals of crystal optics.

5. Gyrotropic media: constitutive relations of the ionosphere and of magnetized ferrites. TheAppleton-Hartree formula

6. Faraday rotation and its applications.

7. Circular birefringence vs. reciprocity in anisotropic media

8. Introduction to the backscattering analysis of optical fibers.

References:

[1] C. G. Someda, Electromagnetic Waves, 2nd Edition, CRC Taylor & Francis, Boca Raton, FL,2006

[2] M. Born and E. Wolf, Principles of Optics: Electromagnetic Theory of Propagation, Interferenceand Diffraction of Light, 6th Ed., Pergamon Press, 1986.

[3] J. A. Kong, Electromagnetic Wave Theory, 2nd Ed., Wiley, 1990.

[4] J.F. Nye, Physical Properties of Crystals: Their Representation by Tensors and Matrices, pa-perback edition, Clarendon Press, 1985.

[5] C.G. Someda and G. I. Stegeman, eds., Anisotropic and Nonlinear Waveguides, Elsevier, 1992.

Time table: Course of 16 hours (two lectures of two hours each per week). Class meets everyWednesday and Friday from 10:30 to 12:30. First lecture on Wednesday, June 8, 2011. RoomDEI/G (3-rd floor, Dept. of Information Engineering, via Gradenigo Building).

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Course requirements: Basic knowledge of e.m. wave propagation in linear isotropic media.Fundamentals of linear algebra.

Examination and grading: Weekly homework assignment. Final mini-project.

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11 Game Theory for Information Engineering

Instructor: Leonardo Badia, e-mail: [email protected]

Aim: Nowadays, micro-economics instruments have exited their traditional scope and have pen-etrated into many other research areas. The introduction of such concepts and techniques withinscientific reasoning is becoming more and more common, and this especially applies to subjectsrelated to technology and applications, such as information engineering, which are impacted bybusiness models and customer choices. Game-theory has since long represented one of the mostinteresting branches of micro-economics, particularly due to its wide range of applications and itsability of giving a mathematical formulation to a plethora of problems. Even though the convergenceof game-theory and information engineering problems looks very promising, several issues are stillopen and it is reasonable to think of them as main research directions in the immediate future. Forthese reasons, it is important for an information engineer to be aware of game-theoretical conceptsand instruments, while at the same time, whenever possible, being capable to bring them into use.The knowledge of game-theory fundamentals enables not only the understanding of several contri-butions recently appeared in the technical literature, but also to identify possible developments ofthis pioneering work and solutions to the problems they have found.

Topics: The list of topics covered in the course is as follows. First part (game-theory):

• Equilibrium concepts. Pareto dominance.

• Choices and utilities. Rationality. Indifference.

• Games. Non-cooperation. Strategies.

• Nash equilibrium. The Prisoner’s dilemma.

• Pure and mixed strategies. Dominance of strategies. Multiple equilibria.

• Dynamic games. Repeated games.

• Cooperation. Pricing. Imperfect/incomplete information. Bayesian equilibrium.

• Signaling. Beliefs.

Second part (application to information engineering):

• Resource sharing, cooperation versus competition.

• Game-theoretic power control and admission control.

• ALOHA as a non cooperative game.

• The price of selfish routing. The Forwarder’s dilemma.

• Cooperation enforcement in communication networks.

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• Optimization problems in game-theory. How to solve them, and how complex it is.

• Future developments and applications.

References:

[1] R. Gibbons, “Game theory for applied economists.”

[2] H. R. Varian, “Microeconomic Analysis.”

[3] M. J. Osborne and A. Rubinstein, ”A Course in Game Theory.”

Further material presented during the course (basically, up-to-date articles recently appeared inthe literature).

Time table: Course of 20 hours (2 two-hours lectures per week). Class meets every Wednesdayand Friday from 10:30 to 12:30. First lecture on Wednesday, February 23, 2011. Room DEI/G(3-rd floor, Dept. of Information Engineering, via Gradenigo Building).

Course requirements: Very basic principles of calculus (typically, already available after twomath undergraduate courses). Basic principles of optimization (a dedicated course on optimizationtheory might help, although it is not strictly necessary, if some optimization principles are presentedin basic math courses). A basic course in Computer Networks and/or Communication Networksis required. The students should master for example concepts like: Ad Hoc Networks, RoutingProtocol, ALOHA, Collision Detection. Expertise in the field of Cognitive Networks is useful, butnot mandatory.

Examination and grading: The examination is based on a written proposal and an oral presen-tation. The written part, in the form of an extended abstract limited to about 3 pages includingreferences, should discuss a possible development and application of the topics explored by thecourse. The student is free to relate it to its own research goals. The oral part concerns a presenta-tion of a recent research paper on course-related subjects. According to the students’ availability,this presentation can be given in front of the class. Also for this part the students are invited to re-late to their own research activity and are free to present a paper of their choice, if compatible withthe course’s scope. The presentation is limited to a 20-minute conference-like (aided with slides)setup, plus questions from the teacher and the audience (if present). The grading is determinedbased on the relevance and technical depth of the proposal, the quality of the oral presentation,and the ability to relate to the course topics.

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12 Harnessing Randomness in Information Theory

Instructor: Matthieu Bloch, School of Electrical and Computer Engineering, Georgia Institute ofTechnology e-mail: [email protected]

Aim: The objective of this course is to study “randomness processing” in information theory,broadly defined as the problem of simulating or transforming stochastic processes. Randomnessplays a key role in many information-theoretic problems such as coordination in networks andinformation-theoretic security. This course will cover several fundamental results related to ran-domness processing and will build upon these results to tackle more complicated problems such assecret-key agreement from common randomness and secure communication over noisy channels.

Topics:

1. Basic tools of information theory

2. Source resolvability and intrinsic randomness

3. Channel resolvability and channel intrinsic randomness

4. Secrecy from resolvability and intrinsic randomness

5. Common information of random variables

6. Coordination capacity

References:

[1] R. Ahlswede and I. Csiszar. Common Randomness in Information Theory and Cryptography.I. Secret Sharing. IEEE Transactions on Information Theory, 39(4):1121–1132, July 1993.

[2] M. Bloch and J. N. Laneman. On the Secrecy Capacity of Arbitrary Wiretap Channels. InProceedings of 46th Allerton Conference on Communication, Control, and Computing, pages818–825, Monticello, IL, September 2008.

[3] Matthieu Bloch. Channel Intrinsic Randomness. In Proc. of IEEE International Symposiumon Information Theory, pages 2607–2611, Austin, TX, June 2010.

[4] Imre Csiszar. Almost Independence and Secrecy Capacity. Problems of Information Transmis-sion, 32(1):40–47, January-March 1996.

[5] P. Cuff. Communication requirements for generating correlated random variables. In Proc.IEEE International Symposium on Information Theory ISIT 2008, pages 1393–1397, 2008.

[6] Te Sun Han. Information-Spectrum Methods in Information Theory. Springer, 2002.

[7] T.S. Han and S. Verdu. Approximation Theory of Output Statistics. IEEE Trans. Inf. Theory,39(3):752–772, May 1993.

[8] A. Wyner. The Common Information of Two Dependent Random Variables. IEEE Trans. Inf.Theory, 21(2):163–179, March 1975.

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[9] A. D. Wyner. The Wire-Tap Channel. Bell System Technical Journal, 54(8):1355–1367, October1975.

Time table: Course of 12 hours (two lectures of two hours each per week). Class meets everyWednesday and Friday from 14:30 to 16:30. First lecture on Wednesday, February 16, 2011. RoomDEI/G (3-rd floor, Dept. of Information Engineering, via Gradenigo Building).

Course requirements: Basic notions of information theory (entropy, mutual information, codingtheorems) and multi-user information theory. More advanced material will be reviewed in classwhen relevant.

Examination and grading: Each student must submit a report based on one or several articlesrelated to the material covered in class. Grading will be based on conciseness, precision andrelevance of report.

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13 Information-theoretic Methods in Security

Instructor: Nicola Laurenti, Department of Information Engineering, Univ. of Padova, e-mail:[email protected]

Aim: To provide the students with an information theoretic framework that will allow formalmodeling, and understanding fundamental performance limits, in several security-related problems

Topics:

Measuring information. Review of basic notions and results in information theory: entropy, equiv-ocation, mutual information, channel capacity.

The Holy Grail of perfect secrecy. Shannon’s cipher system. Perfect secrecy. Ideal secrecy. Practi-cal secrecy.

Secrecy without cryptography. The wiretap channel model. Rate-equivocation pairs. Secrecy ca-pacity for binary, Gaussian and fading channel models.

Security from uncertainty. Secret key agreement from common randomness on noisy channels. In-formation theoretic models and performance limits of quantum cryptography.

The gossip game. Broadcast and secrecy models in multiple access channels. The role of trustedand untrusted relays.

A cipher for free? Information-theoretic security of random network coding.

Who’s who? An information theoretic model for authentication in noisy channels. Signatures andfingerprinting.

Writing in sympathetic ink. Information theoretic models of steganography, watermarking andother information hiding techniques.

The jamming game. Optimal strategies for transmitters, receivers and jammers in Gaussian, fadingand MIMO channels.

Leaky buckets and pipes. Information leaking and covert channels. Timing channels.

The dining cryptographers. Privacy and anonymity. Secure multiparty computation.

Information theoretic democracy. Privacy, reliability and verifiability in electronic voting systems.

References: Y. Liang, H.V. Poor, and S. Shamai (Shitz), Information Theoretic Security, Now,2007.

M. Bloch, J. Barros, Physical-Layer Security: from Information Theory to Security EngineeringCambridge University Press, 2011.

A list of reference papers for each lecture will be provided during class meetings.

Time table: Course of 20 hours (two lectures of two hours each per week). Class meets everyWednesday and Friday from 10:30 to 12:30, starting on Wednesday, October 5 and ending on Friday,November 4, 2011. Room DEI/G (3-rd floor, Dept. of Information Engineering, via GradenigoBuilding).

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Course requirements: Basic notions of Information Theory

Examination and grading: Each student must submit a project, and grading will be based on itsevaluation. I encourage students to work from an information theoretic point of view on a securityproblem related to their research activities.

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14 Introduction to Quantum Optics I: quantum information andcommunication

Instructor: Alexander Sergienkoe-mail: [email protected]

Aim: This part is intended to provide the basic concepts of Quantum Information and QuantumCommunications. It will start with review of the underlying concepts of quantum physics. Itwill be followed by the discussion of entanglement, quantum interference, quantum computation,and quantum communication. Specifics of practical implementation of quantum bits and quantumlogic gates in different physical environments will be considered. Existing problems of experimentalimplementation associated with detrimental effects of decoherence will be discussed.The Course in organized in the Framework of the “Progetto Strategico di Ateneo” QuantumFuture.

Topics:

1. Review of Quantum Mechanics;

2. quantization of EM field;

3. statistics of radiation;

4. entanglement;

5. quantum interferometry;

6. principles of quantum computation and quantum key distribution.

References:

C. Gerry, P. Knight, “Introductory Quantum Optics”, (Cambridge 2005)

Michel Le Bellac, “A Short Introduction to Quantum Information and Quantum Computation”,(Cambridge 2006)

Additional reading:

A. V. Sergienko ed. “Quantum Communications and Cryptography”, (CRC Press, Taylor & FrancisGroup 2006).

G. S. Jaeger, “Quantum Information: An Overview”, Springer (2010).

Time table: Course of 12 hours (two lectures of two hours each per week). Class meets everyMonday and Wednesday from 10:30 to 12:30. First lecture on Monday, March 28, 2011. RoomDEI/G (3-rd floor, Dept. of Information Engineering, via Gradenigo Building).

Course requirements: Basic concept of Quantum Physics.

Examination and grading: Homework and final exam.

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15 Introduction to Quantum Optics II: quantum measurement,imaging, and metrology

Instructor: Alexander Sergienkoe-mail: [email protected]

Aim: The second part of this course is intended to provide the overview of novel technologicalapproaches based on the use of quantum correlations and quantum entanglement. Specifics ofoptical implementation of qubits and linear-optical quantum gates will be discussed. This course willreview specific concepts of quantum-optical state engineering and design of non-traditional quantummeasurement devices that outperform their classical counterparts. Several such novel approachesas quantum imaging, super-resolution quantum phase measurement, dispersion cancelation, andcorrelated imaging and microscopy will be discussed.The Course in organized in the Framework of the “Progetto Strategico di Ateneo” QuantumFuture.

Topics:

1. Generation and detection of entangled states;

2. linear-optical quantum state engineering;

3. high-resolution quantum interferometry;

4. entangled and correlated quantum imaging;

5. dispersion and aberration cancellation in biological imaging;

6. correlated confocal microscopy.

References:

C. Gerry, P. Knight, “Introductory Quantum Optics”, (Cambridge 2005).

Michel Le Bellac, “A Short Introduction to Quantum Information and Quantum Computation”,(Cambridge 2006).

Additional reading:

A. V. Sergienko ed. “Quantum Communications and Cryptography”, (CRC Press, Taylor & FrancisGroup 2006).

G. S. Jaeger, “Quantum Information: An Overview”, Springer (2010).

Time table: Course of 12 hours (two lectures of two hours each per week). Class meets everyMonday and Wednesday from 10:30 to 12:30. First lecture on Monday, May 9, 2011. Room DEI/G(3-rd floor, Dept. of Information Engineering, via Gradenigo Building).

Course requirements: Basic concept of Quantum Physics.

Examination and grading: Homework and final exam.

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16 Multiple antennas in wireless communications

Instructors: Dr. Luca Sanguinetti and Dr. Giacomo Bacci, Dept. of Information Eng., Universityof Pisa, Italy (e-mail: [email protected], [email protected]).

Aim:Wireless local area networks are currently supplementing or replacing wired networks in homesand offices. In addition, a large number of new applications, including wireless sensor networks,automated highways and factories, smart phones and appliances, and remote telemedicine, areemerging from research ideas to concrete systems. On the other hand, the design of an efficient,reliable, and robust wireless communication network for each of these emerging applications posesmany technical challenges. For these reasons, multiple-antenna radio systems have gained a lot ofinterest thanks to their potential benefits in terms of capacity and/or diversity gain. The aim ofthis series of lectures is to provide an overview of the fundamental information-theoretic results andpractical implementation issues of multiple-antenna networks operating under various conditionsof channel state information.

Topics:

1. Introduction and motivation.

2. Capacity of multiple antenna systems:

(a) Deterministic channel

(b) Fading channel

3. Space-time coding: principles and applications.

4. Fundamentals of system design with multiple transmit and receive antennas:

(a) Channel known at the receiver

(b) Channel known at the transmitter

5. From single-user to multi-user MIMO communications.

References: Reading material will be provided during the course. In addition, a list of excellentbooks on the subject are reported below.

[1] A. Paulraj, R. Nabar and D. Gore, Introduction to Space-Time Wireless Communications,Cambridge University Press, May 2003.

[2] A. Goldsmith, Wireless Communications, Cambridge University Press, Aug. 2005.

[3] D. Tse and P. Viswanath, Fundamentals of Wireless Communication, Cambridge UniversityPress, May 2005.

[4] E. Biglieri, R. Calderbank, A. Constantinides, A. Goldsmith, A. Paulraj, H. V. Poor, MIMOWireless Communications, Cambridge University Press, Jan. 2007.

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Time table: Course of 20 hours (2 two-hour lectures per week). Classes on Mondays 14:30 –16:30, and Tuesdays 10:30 – 12:30. First lecture on Monday, January 17, 2010. Last lecture will beon Tuesday, February 15, 2010. Room DEI/G (3-rd floor, Dept. of Information Engineering, viaGradenigo Building).

Course requirements: Basic knowledge of wireless communications.

Examination and grading: Homeworks and final oral exam.

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17 Physical models for the numerical simulation of semiconductordevices

Instructor: Prof. Giovanni Verzellesi, Dipartimento di Scienze e Metodi dellIngegneria, Universityof Modena and Reggio Emilia.e-mail: [email protected]: http://www.dismi.unimore.it/Members/gverzellesi

Aim: This course is intended to provide an introductory coverage on charge transport in semicon-ductors and on the physical models adopted in numerical device simulators, which are nowadaysroutinely adopted for the design and optimization of device fabrication processes (Technology Com-puter Aided Design or TCAD).

Topics: The course will cover the following topics:

a) Fundamentals of quantum mechanics and semiconductor physics: Schrdinger equation, Ehren-fest theorem, wavepackets, crystals, electrons in periodic structures, scattering mechanisms.

b) Charge transport in semiconductors: Boltzmann transport equation, momentum method,hydrodynamic model, drift-diffusion model, drift-diffusion model for non-uniform semicon-ductors, models for simulation of nano-scale devices.

c) Numerical device simulation: technology CAD, input and output data of device simulators,discretization of drift-diffusion equations, boundary conditions, physical models (mobility,generation-recombination effects, deep levels).

References: M. Lundstrom, Fundamentals of carrier transport, Modular Series on Solid StateDevices vol. X, Addison-Wesley Publ. Company, ISBN 0-201-18436-2, 1992. K. Hess, Advancedtheory of semiconductor devices, IEEE Press, ISBN 0-7803-3479-5, 2000.

Time table: Course of 20 hrs (4 hours per week). Lectures (2 hours) on Thursday 4:30 – 6:30 andFriday 2:30 – 4:30. First lecture on Thursday, October 27, 2011. Room DEI/G (3-rd floor, Dept.of Information Engineering, via Gradenigo Building).

Course requirements: Background at a graduate level on semiconductor devices.

Examination and grading: Final written test.

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18 Statistical Methods

Instructor: Lorenzo Finesso, Istituto di Ingegneria Biomedica, ISIB-CNR, Padovae-mail: [email protected]

Aim: The course will present a survey of statistical techniques which are important in applications.The unifying power of the information theoretic point of view will be stressed.

Topics:

Background material. The noiseless source coding theorem will be quickly reviewed in order tointroduce the basic notions of entropy and informational divergence (Kullback-Leibler distance) ofprobability measures. The analytical and geometrical properties of the divergence will be presented.

Divergence minimization problems. Three basic minimization problems will be posed and, on simpleexamples, it will be shown that they produce the main methods of statistical inference: hypothesistesting, maximum likelihood, maximum entropy.

Multivariate analysis methods. Study of the probabilistic and statistical aspects of the three mainmethods: Principal Component Analysis (PCA), Canonical Correlations (CC) and Factor Analysis(FA). In the spirit of the course these methods will be derived also via divergence minimization.Time permitting there will be a short introduction to the Nonnegative Matrix Factorization methodas an alternative to PCA to deal with problems with positivity constraints.

EM methods. The Expectation-Maximization method was introduced as an algorithm for thecomputation of the Maximum Likelihood (ML) estimator with partial observations (incompletedata). We will derive the EM method for the classic mixture decomposition problem and alsointerpret it as an alternating divergence minimization algorithm a la Csiszar Tusnady.

Hidden Markov models. We will introduce the simple yet powerful class of Hidden Markov models(HMM) and discuss parameter estimation for HMMs via the EM method.

The MDL method. The Minimum Description Length method of Rissanen will be presented as ageneral tool for model complexity estimation.

References: A set of lecture notes and a list of references will be posted on the web site of thecourse.

Time table: Course of 24 hours (two lectures of two hours each per week). Class meets everyTuesday and Thursday from 10:30 to 12:30. First lecture on Tuesday, June 14, 2011. Room DEI/G(3-rd floor, Dept. of Information Engineering, via Gradenigo Building).

Course requirements: Basics of Probability Theory and Linear Algebra.

Examination and grading: homework assignments and take-home exam.

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19 Stochastic (Ordinary and Partial) Differential Equations

Instructor: Paolo Guiotto (Dept. Pure and Applied Mathematics, Universita di Padova), e-mail:[email protected]

Aim: Introduce to the theory of stochastic equations in finite and infinite dimensions.

Topics: The course is divided in two parts. In the Basic Part the aim is to give a quick rigorousintroduction to the basic tools of stochastic analysis: brownian motion, stochastic integrals andstochastic differential equations in finite dimensions (which is the starting point for several modelsin applications). In the Advanced Part the aim is to introduce to the more sophisticated frameworkof stochastic equations in infinite dimensions. This setting present several applications to stochasticPDE’s (a good model for turbulence) and spin systems.Basic Part. 1. Brownian Motion — definition, the Levy–Ciesielskii construction, properties ofthe paths of the BM. 2. Stochastic Integral — the Wiener integral, predictable processes, martin-gales, Doob inequalities, construction and properties of the Ito integral. 3. Stochastic Calculus— Ito formula and applications. 4. Stochastic Differential Equations — existence, uniqueness,properties of the paths of a solution, Markov property of the stochastic flow.Advanced Part. 1. Introduction to stochastic analysis in Hilbert spaces: nuclear brownianmotion, Ito integral and Ito formula. 2. Elements of semigroup theory. 3. Stochastic evolutionequation in Hilbert space. 4. Applications.

References: Lecture notes will be available with further references.

Time table: Course of 20 hours (two lectures of two hours each per week). Class meets everyTuesday and Thursday from 10:30 to 12:30. First lecture on Tuesday, March 1, 2011. Room DEI/G(3-rd floor, Dept. of Information Engineering, via Gradenigo Building).

Course requirements: Solid knowledge of basic Kolmogorov probability (measure theory based)and basic functional analysis (Hilbert spaces, bounded linear operators, Lp spaces).

Examination and grading: Homeworks on the Basic Part, seminar on a subject assigned by theInstructor on the Advanced Part.

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20 Subspace Techniques for the Identification of Linear Systems

Instructor: Giorgio Picci, e-mail: [email protected]

Aim: To illustrate the basic principles and the main techniques available today for the identifi-cation of linear multivariable systems. Traditional parameter-optimization-based methods (suchas Prediction Error Methods) are difficult to apply to multivariable system identification due tothe need of canonical parametrizations and may lead to unreliable results because of local minimaand bad conditioning. For this reason, new identification techniques have recently been developedcalled “subspace methods” which have been demonstrated to be a robust and reliable alternativeto classical parameter optimization algorithms. The theoretical foundation of these methods layin stochastic realization theory and modern numerical linear algebra. The computational effortrequired by subspace identification algorithms is generally lower and the implementation is mucheasier. Experimental evidence shows also that the accuracy of subspace methods is generally com-parable to that of the optimization-based procedures. However there are also some drawbacks.First a plethora of algorithms with different acronyms (such as N4SID , PI MOESP, PO MOESP,CCA etc.) is available on the market which makes a motivated choice very difficult for the un-experienced user. It is not clear what differences there are among those algorithms and what arethe natural application areas. Second a comprehensive statistical analysis of subspace methods isstill to be developed. In fact the statistical analysis of subspace methods is still an active area ofresearch world-wide. This course aims also to provide a pointer to these research areas.

Topics:

1. LINEAR STOCHASTIC MODELS FOR RANDOM PROCESSES: basic facts, the stateprocess, minimality, Lyapunov equations for the state covariance, innovation representation,forward and backward representations, the forward and backward Kalman filter

2. STOCHASTIC REALIZATION AND THE LMI: rational spectral factorization, minimalspectral factors and the Linear Matrix Inequality, relation with Riccati equations and Kalmanfilter

3. HANKEL MATRICES AND REALIZATION BY CANONICAL CORRELATION ANAL-YSIS (CCA): CCA for random vectors, the Singular Value Decomposition (SVD), singularvalues of a linear systems, partial realization via Ho-Kalman, Positivity issues

4. SUBSPACE IDENTIFICATION OF TIME SERIES: Basic ideas, dealing with finite data: fi-nite interval stochastic realization, Hankel matrices from sample covariances and their approx-imate factorization, estimating A,C, C by solving the shift-invariance equations, estimatingthe B,D parameters, numerical aspects: the LQ decomposition

5. EXOGENOUS SIGNALS AND FEEDBACK MODELS: stochastic state-space models witinputs, joint innovation models, identifiability, abstract state space construction (obliquesplitting), state space construction with feedback

6. SUBSPACE IDENTIFICATION WITH INPUTS: Basic principles, the problem of finite data,finite-interval realization, the N4SID procedure, MOESP, consistency and ill-conditioning,asymptotic variance expressions, identification with feedback, the predictor model, descriptionof various algorithms.

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References:

1. Chiuso, A. and G. Picci (2004). On the ill-conditioning of subspace identification with inputs.Automatica, 40, 575-589.

2. A. Chiuso and G. Picci (2005), “Consistency Analysis of some Closed-loop Subspace Identi-fication Methods”, Automatica: special issue on System Identification, 41 pp. 377-391.

3. T. Katayama (2005) Subspace Methods for System Identification, Springer Verlag

4. A. Lindquist and G. Picci, Canonical Correlation Analysis, Approximate Covariance Exten-sion and Identification of Stationary Time Series, Automatica, 32 (1996), 709-733.

5. J. Qin, L. Ljung (2003) “Closed-Loop Subspace Identification with Innovation Estimation”,in Proc. of the IFAC Int. Symposium on System Identification (SYSID), Rotterdam, 2003.

6. P. van Overschee and B. De Moor, Subspace algorithms for stochastic identification problem,Automatica 3 (1993), 649-660.

7. Overschee, P. Van and B. De Moor (1994). N4SID: Subspace algorithms for the identificationof combined deterministic– stochastic systems. Automatica 30, 75–93.

8. Overschee, P. Van and B. De Moor (1996). Subspace Identification for Linear Systems, KluwerAcademic Pub.

Time table: Course of 20 hours (two lectures of two hours each per week). Class meets everyTuesday and Thursday from 14:30 to 16:30. First lecture on Tuesday, March 1, 2011. Room DEI/G(3-rd floor, Dept. of Information Engineering, via Gradenigo Building).

Course requirements: Background on Linear systems and second order Random Processes.

Examination and grading: Homework + final exam.

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